A computer system models customer response using observable data. The
observable data includes transaction, product, price, and promotion. The
computer system receives data observable from customer responses. A set
of factors including customer traffic within a store, selecting a
product, and quantity of selected product is defined as expected values,
each in terms of a set of parameters related to customer buying decision.
A likelihood function is defined for each of the set of factors. The
parameters are solved using the observable data and associated likelihood
function. The customer response model is time series of unit sales
defined by a product combination of the expected value of customer
traffic and the expected value of selecting a product and the expected
value of quantity of selected product. A linear relationship is given
between different products which includes a constant of proportionality
that determines affinity and cannibalization relationships between the
products.